Detecting Community Structures in Social Networks with Particle Swarm Optimization

Abstract

Community detection in social networks is usually considered as an objective optimization problem. Due to the limitation of the objective function, the global optimum cannot describe the real partition well, and it is time consuming. In this paper, a novel PSO (particle swarm optimization) algorithm based on modularity optimization for community detection in social networks is proposed. Firstly, the algorithm takes similarity-based clustering to find core areas in the network, and then a modified particle swarm optimization is performed to optimize modularity in a new constructed weighted network which is compressed from the original one, and it is equivalent to optimize modularity in the original network with some restriction. Experiments are conducted in the synthetic and four real-world networks. The experimental results show that the proposed algorithm can effectively extract the intrinsic community structures of social networks.